Hidden Link Prediction In Stochastic Social Networks
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Hidden Link Prediction in Stochastic Social Networks
Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers.
Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI, July 3–4, 2024, London, UK
This book offers an in-depth exploration of cutting-edge research across the interconnected fields of computing, communication, cybersecurity, and artificial intelligence. It serves as a comprehensive guide to the technologies shaping our digital world, providing both a profound understanding of these domains and practical strategies for addressing their challenges. The content is drawn from the International Conference on Computing, Communication, Cybersecurity and AI (C3AI 2024), held in London, UK, from July 3 to 4, 2024. The conference attracted 66 submissions from 17 countries, including the USA, UK, Canada, Brazil, India, China, Germany, and Spain. Of these, 47 high-calibre papers were rigorously selected through a meticulous review process, where each paper received three to four reviews to ensure quality and relevance. This book is an essential resource for readers seeking a thorough and timely review of the latest advancements and trends in computing, communication, cybersecurity, and artificial intelligence.
Social Network Analysis
Author: Mohammad Gouse Galety
language: en
Publisher: John Wiley & Sons
Release Date: 2022-04-28
SOCIAL NETWORK ANALYSIS As social media dominates our lives in increasing intensity, the need for developers to understand the theory and applications is ongoing as well. This book serves that purpose. Social network analysis is the solicitation of network science on social networks, and social occurrences are denoted and premeditated by data on coinciding pairs as the entities of opinion. The book features: Social network analysis from a computational perspective using python to show the significance of fundamental facets of network theory and the various metrics used to measure the social network. An understanding of network analysis and motivations to model phenomena as networks. Real-world networks established with human-related data frequently display social properties, i.e., patterns in the graph from which human behavioral patterns can be analyzed and extracted. Exemplifies information cascades that spread through an underlying social network to achieve widespread adoption. Network analysis that offers an appreciation method to health systems and services to illustrate, diagnose, and analyze networks in health systems. The social web has developed a significant social and interactive data source that pays exceptional attention to social science and humanities research. The benefits of artificial intelligence enable social media platforms to meet an increasing number of users and yield the biggest marketplace, thus helping social networking analysis distribute better customer understanding and aiding marketers to target the right customers. Audience The book will interest computer scientists, AI researchers, IT and software engineers, mathematicians.